In a world where AI is making data available to nearly every employee, there's never been a time when it's more important for non-technical people to become data literate. The problem for many of these individuals, however, is they don't even know where to start.

A good place to start is a good old fashioned basic statistics course. This will give you the foundational language and understanding of data concepts needed to really get value from analytics. The great thing for people looking for these kinds of courses is the sheer number of online options available today. And believe it or not, statistics taught well is pretty easy and a lot of fun.

A second way to increase your data literacy is simply to get access to data. If you don't have access today, go and ask your manager for it. If you have access to data but don't know to use it, don't be afraid to ask for help. Chances are you have a power user closer to you than you think who would be more than happy to share their knowledge with you. Lastly, if you notice your organization isn't set up to provide data access don't be afraid to ask for new technologies to make that happen. You'd be shocked at how requests for more self service from business users can instigate change for IT departments.

Third, ask for data more often. One of the most important parts of learning a new language is actually using it. Learning the language of data is no different. You need to actually use it to learn it. So whenever you see a decision being made, whether it's by your manager, a colleague, or one of your reports, ask for the data that backs up their decision. If you're making the decision, ask yourself what data you've used to arrive at this conclusion. You need to create an internal demand for data, and expect it in everything you do.

It's also important to measure yourself if you want to become more data literate. Ask yourself: how you measure your success on the job. In roles like sales or marketing it may be obvious--you're measured on customer attainment or the number of clicks in a certain ad campaign. Bring the same mentality of self-measurement to data literacy. Create a view into your corporate performance that's data driven, and use that to measure your performance. It will help you think things through data driver.

Most importantly, don't think of data literacy as a technical issue. It's a business issue. With artificial intelligence, most of the technical requirements for interacting with data are dropping away. Frame the issue to yourself as a critical business need, and it'll help drive your commitment to improving.

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